In the cross-domain experiments, we train the GaitSet model on the training set of one source dataset and evaluation the trained model on the testing set of one target dataset.
For CASIA-B, OU-LP, and Gait3D, we use the official train/test split for training and testing.
Because the test set of GREW [1]} is not released publicly for evaluation, we randomly sample 1,000 IDs from the training set of GREW for evaluation.
For the sampled 1,000 IDs, we further randomly select one sequence from each ID to build the query set with 1,000 sequences, while the rest of the sequences becomes the gallery set with 4,095 sequences.
We utilize Rank-1 (R-1), Rank-5 (R-5), and mAP as the evaluation metrics.
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